The journal of finance , tập 66, số 1, 2011 2

Vol. 66

February 2011

No. 1

Editor
CAMPBELL R. HARVEY
Duke University

Co-Editor
JOHN GRAHAM
Duke University

Associate Editors
ANAT R. ADMATI
Stanford University

ANDREW METRICK
Yale University

ANDREW ANG
Columbia University

TOBIAS J. MOSKOWITZ
University of Chicago

KERRY BACK
Rice University

DAVID K. MUSTO

University of Pennsylvania

MALCOLM BAKER
Harvard University

TERRANCE ODEAN
University of California, Berkeley

NITTAI K. BERGMAN
Massachusetts Institute of Technology

CHRISTINE A. PARLOUR
University of California, Berkeley

HENDRIK BESSEMBINDER
University of Utah

´
L˘ UBOS˘ PASTOR
University of Chicago

MICHAEL W. BRANDT
Duke University
ALON BRAV
Duke University
MARKUS K. BRUNNERMEIER
Princeton University
DAVID A. CHAPMAN
Boston College
JENNIFER S. CONRAD

University of North Carolina
FRANCESCA CORNELLI
London Business School
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INSEAD
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University of California, Irvine
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University of Pennsylvania
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Massachusetts Institute of Technology
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London Business School
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University of North Carolina
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University of California, Berkeley
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University of Minnesota

Business Manager
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University of California, Berkeley

Assistant Editor
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Volume 66

CONTENTS for FEBRUARY 2011

No. 1

ARTICLES
Does Algorithmic Trading Improve Liquidity?
TERRENCE HENDERSHOTT, CHARLES M. JONES,
and ALBERT J. MENKVELD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
When Is a Liability Not a Liability? Textual Analysis,
Dictionaries, and 10-Ks
TIM LOUGHRAN and BILL MCDONALD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
The Causal Impact of Media in Financial Markets
JOSEPH E. ENGELBERG and CHRISTOPHER A. PARSONS . . . . . . . . . . . . . . . . . . . . 67
Leverage, Moral Hazard, and Liquidity
VIRAL V. ACHARYA and S. VISWANATHAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Stock Market Liquidity and the Business Cycle
RANDI NÆS, JOHANNES A. SKJELTORP, and BERNT ARNE ØDEGAARD . . . . . 139
Asset Pricing with Garbage

ALEXI SAVOV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Derivative Pricing with Liquidity Risk: Theory and Evidence
from the Credit Default Swap Market
DION BONGAERTS, FRANK DE JONG, and JOOST DRIESSEN . . . . . . . . . . . . . . . . 203
The Decision to Privatize: Finance and Politics
I. SERDAR DINC and NANDINI GUPTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Why Do Mutual Fund Advisory Contracts Change? Performance,
Growth, and Spillover Effects
JEROLD B. WARNER and JOANNA SHUANG WU . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
Style-Related Comovement: Fundamentals or Labels?
BRIAN H. BOYER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307

MISCELLANEA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333


THE JOURNAL OF FINANCE • VOL. LXVI, NO. 1 • FEBRUARY 2011

Does Algorithmic Trading Improve Liquidity?
TERRENCE HENDERSHOTT, CHARLES M. JONES, and ALBERT J. MENKVELD∗
ABSTRACT
Algorithmic trading (AT) has increased sharply over the past decade. Does it improve
market quality, and should it be encouraged? We provide the first analysis of this
question. The New York Stock Exchange automated quote dissemination in 2003, and
we use this change in market structure that increases AT as an exogenous instrument
to measure the causal effect of AT on liquidity. For large stocks in particular, AT
narrows spreads, reduces adverse selection, and reduces trade-related price discovery.
The findings indicate that AT improves liquidity and enhances the informativeness
of quotes.

TECHNOLOGICAL CHANGE HAS REVOLUTIONIZED the way financial assets are

traded. Every step of the trading process, from order entry to trading venue to
back office, is now highly automated, dramatically reducing the costs incurred
by intermediaries. By reducing the frictions and costs of trading, technology
has the potential to enable more efficient risk sharing, facilitate hedging, improve liquidity, and make prices more efficient. This could ultimately reduce
firms’ cost of capital.
Algorithmic trading (AT) is a dramatic example of this far-reaching technological change. Many market participants now employ AT, commonly defined
as the use of computer algorithms to automatically make certain trading decisions, submit orders, and manage those orders after submission. From a
starting point near zero in the mid-1990s, AT is thought to be responsible for
as much as 73 percent of trading volume in the United States in 2009.1
There are many different algorithms, used by many different types of
market participants. Some hedge funds and broker–dealers supply liquidity
∗ Hendershott is at Haas School of Business, University of California Berkeley. Jones is at
Columbia Business School. Menkveld is at VU University Amsterdam. We thank Mark van Achter,
Hank Bessembinder, Bruno Biais, Alex Boulatov, Thierry Foucault, Maureen O’Hara, S´ebastien
Pouget, Patrik Sandas, Kumar Venkataraman, the NASDAQ Economic Advisory Board, and seminar participants at the University of Amsterdam, Babson College, Bank of Canada, CFTC, HEC
Paris, IDEI Toulouse, Southern Methodist University, University of Miami, the 2007 MTS Conference, NYSE, the 2008 NYU-Courant algorithmic trading conference, University of Utah, the
2008 Western Finance Association meetings, and Yale University. We thank the NYSE for providing system order data. Hendershott gratefully acknowledges support from the National Science
Foundation, the Net Institute, the Ewing Marion Kauffman Foundation, and the Lester Center
for Entrepreneurship and Innovation at the Haas School at UC Berkeley. Menkveld gratefully
acknowledges the College van Bestuur of VU University Amsterdam for a VU talent grant.
1 See “SEC runs eye over high-speed trading,” Financial Times, July 29, 2009. The 73% is an
estimate for high-frequency trading, which, as discussed later, is a subset of AT.

1


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using algorithms, competing with designated market-makers and other liquidity suppliers (e.g., Jovanovic and Menkveld (2010)). For assets that trade on
multiple venues, liquidity demanders often use smart order routers to determine where to send an order (e.g., Foucault and Menkveld (2008)). Statistical
arbitrage funds use computers to quickly process large amounts of information
contained in the order flow and price moves in various securities, trading at
high frequency based on patterns in the data. Last but not least, algorithms
are used by institutional investors to trade large quantities of stock gradually
over time.
Before AT took hold, a pension fund manager who wanted to buy 30,000
shares of IBM might hire a broker-dealer to search for a counterparty to execute
the entire quantity at once in a block trade. Alternatively, that institutional
investor might have hired a New York Stock Exchange (NYSE) floor broker to
go stand at the IBM post and quietly “work” the order, using his judgment and
discretion to buy a little bit here and there over the course of the trading day
to keep from driving the IBM share price up too far. As trading became more
electronic, it became easier and cheaper to replicate that floor trader with a
computer program doing AT (see Hendershott and Moulton (2009) for evidence
on the decline in NYSE floor broker activity).
Now virtually every large broker-dealer offers a suite of algorithms to its
institutional customers to help them execute orders in a single stock, in pairs
of stocks, or in baskets of stocks. Algorithms typically determine the timing,
price, quantity, and routing of orders, dynamically monitoring market conditions across different securities and trading venues, reducing market impact by
optimally and sometimes randomly breaking large orders into smaller pieces,
and closely tracking benchmarks such as the volume-weighted average price
(VWAP) over the execution interval. As they pursue a desired position, these
algorithms often use a mix of active and passive strategies, employing both
limit orders and marketable orders. Thus, at times they function as liquidity
demanders, and at times they supply liquidity.
Some observers use the term AT to refer only to the gradual accumulation or
disposition of shares by institutions (e.g., Domowitz and Yegerman (2005)). In
this paper, we take a broader view of AT, including in our definition all participants who use algorithms to submit and cancel orders. We note that algorithms

are also used by quantitative fund managers and others to determine portfolio
holdings and formulate trading strategies, but we focus on the execution aspect
of algorithms because our data reflect counts of actual orders submitted and
cancelled.
The rise of AT has obvious direct impacts. For example, the intense activity generated by algorithms threatens to overwhelm exchanges and market
data providers,2 forcing significant upgrades to their infrastructures. But researchers, regulators, and policymakers should be keenly interested in the
broader implications of this sea change in trading. Overall, does AT have

2

See “Dodgy tickers-stock exchanges,” Economist, March 10, 2007.


Does Algorithmic Trading Improve Liquidity?

3

salutary effects on market quality, and should it be encouraged? We provide
the first empirical analysis of this question.
As AT has grown rapidly since the mid-1990s, liquidity in world equity markets has also dramatically improved. Based on these two coincident trends, it
is tempting to conclude that AT is at least partially responsible for the increase
in liquidity. But it is not at all obvious a priori that AT and liquidity should
be positively related. If algorithms are cheaper and/or better at supplying liquidity, then AT may result in more competition in liquidity provision, thereby
lowering the cost of immediacy. However, the effects could go the other way if
algorithms are used mainly to demand liquidity. Limit order submitters grant
a trading option to others, and if algorithms make liquidity demanders better able to identify and pick off an in-the-money trading option, then the cost
of providing the trading option increases, in which case spreads must widen
to compensate. In fact, AT could actually lead to an unproductive arms race,
where liquidity suppliers and liquidity demanders both invest in better algorithms to try to take advantage of the other side, with measured liquidity the
unintended victim.

In this paper, we investigate the empirical relationship between AT and liquidity. We use a normalized measure of NYSE electronic message traffic as a
proxy for AT. This message traffic includes electronic order submissions, cancellations, and trade reports. Because we normalize by trading volume, variation
in our AT measure is driven mainly by variation in limit order submissions
and cancellations. This means that, for the most part, our measure is picking
up variation in algorithmic liquidity supply. This liquidity supply likely comes
both from proprietary traders that are making markets algorithmically and
from buy-side institutions that are submitting limit orders as part of “slice and
dice” algorithms.
We first examine the growth of AT and the improvements in liquidity over a
5-year period. As AT grows, liquidity improves. However, while AT and liquidity
move in the same direction, it is certainly possible that the relationship is not
causal. To establish causality we study an important exogenous event that
increases the amount of AT in some stocks but not others. In particular, we
use the start of autoquoting on the NYSE as an instrument for AT. Previously,
specialists were responsible for manually disseminating the inside quote. This
was replaced in early 2003 by a new automated quote whenever there was a
change to the NYSE limit order book. This market structure provides quicker
feedback to algorithms and results in more electronic message traffic. Because
the change was phased in for different stocks at different times, we can take
advantage of this nonsynchronicity to cleanly identify causal effects.
We find that AT does in fact improve liquidity for large-cap stocks. Quoted
and effective spreads narrow under autoquote. The narrower spreads are a
result of a sharp decline in adverse selection, or equivalently a decrease in the
amount of price discovery associated with trades. AT increases the amount of
price discovery that occurs without trading, implying that quotes become more
informative. There are no significant effects for smaller-cap stocks, but our
instrument is weaker there, so the problem may be a lack of statistical power.


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Surprisingly, we find that AT increases realized spreads and other measures
of liquidity supplier revenues. This is surprising because we initially expected
that if AT improved liquidity, the mechanism would be competition between
liquidity providers. However, the evidence clearly indicates that liquidity suppliers are capturing some of the surplus for themselves. The most natural
explanation is that, at least during the introduction of autoquote, algorithms
had market power. Over a longer time period liquidity supplier revenues decline, suggesting that any market power was temporary, perhaps because new
algorithms require considerable investment and time to build.
The paper proceeds as follows. Section I discusses related literature. Section II describes our measures of liquidity and AT and discusses the need for
an instrumental variables approach. Section III provides a summary of the
NYSE’s staggered introduction of autoquote in 2003. Section IV examines the
impact of AT on liquidity. Section V explores the sources of the liquidity improvement. Section VI studies AT’s relation to price discovery via trading and
quote updating. Section VII discusses and interprets the results, and Section
VIII concludes.
I. Related Literature
Only a few papers address AT directly. For example, Engle et al. (2007) use
execution data from Morgan Stanley algorithms to study the effects on trading
costs of changing algorithm aggressiveness. Domowitz and Yegerman (2005)
study execution costs for a set of buy-side institutions, comparing results from
different algorithm providers. Chaboud et al. (2009) study AT in the foreign
exchange market and focus on its relation to volatility, while Hendershott
and Riordan (2009) measure the contributions of AT to price discovery on the
Deutsche Boerse.
Several strands of literature touch related topics. Most models take the traditional view that one set of traders provides liquidity via quotes or limit orders
and another set of traders initiates a trade to take that liquidity—for either informational or liquidity/hedging reasons. Many assume that liquidity suppliers
are perfectly competitive, for example, Glosten (1994). Glosten (1989) models
a monopolistic liquidity supplier, while Biais et al. (2000) model competing
liquidity suppliers and find that their rents decline as the number increases.

Our initial expectation is that AT facilitates the entry of additional liquidity
suppliers, increasing competition.
The development and adoption of AT also involves strategic considerations.
While algorithms have low marginal costs, there may be substantial development costs, and it may be costly to optimize the algorithms’ parameters for
each security. The need to recover these costs should lead to the adoption of
AT at times and in securities where the returns to adoption are highest (see
Reinganum (1989) for a review of innovation and technology adoption).
As we discuss briefly in the introduction, liquidity supply involves posting
firm commitments to trade. These standing orders provide free trading options to other traders. Using standard option pricing techniques, Copeland


Does Algorithmic Trading Improve Liquidity?

5

and Galai (1983) value the cost of the option granted by liquidity suppliers.
Foucault et al. (2003) study the equilibrium level of effort that liquidity suppliers should expend in monitoring the market to reduce this option’s cost. Black
(1995) proposes a new limit order type that is indexed to the overall market
to minimize picking-off risk. Algorithms can efficiently implement this kind of
monitoring and adjustment of limit orders.3 If AT reduces the cost of the free
trading option implicit in limit orders, then measures of adverse selection depend on AT. If some users of AT are better at avoiding being picked off, they can
impose adverse selection costs on other liquidity suppliers as in Rock (1990)
and even drive out other liquidity suppliers.
AT may also be used by traders who are trying to passively accumulate or
liquidate a large position.4 There are optimal dynamic execution strategies for
such traders. For example, Bertsimas and Lo (1998) find that, in the presence of
temporary price impacts and a trade completion deadline, orders are optimally
broken into pieces so as to minimize cost.5 Many brokers incorporate such
considerations into the AT products that they sell to their clients. In addition,
algorithms monitor the state of the limit order book to dynamically adjust their

trading strategies, for example, when to take and offer liquidity (Foucault et al.
(2010)).
II. Data
We start by characterizing the time-series evolution of AT and liquidity for a
sample of NYSE stocks over the 5 years from February 2001 through December
2005. We limit attention to the post-decimalization regime because the change
to a one-penny minimum tick was a structural break that substantially altered
the entire trading landscape, including liquidity metrics and order submission
strategies. We end in 2005 because substantial NYSE market structure changes
occur shortly thereafter.
We start with a sample of all NYSE common stocks that can be matched in
both the Trades and Quotes (TAQ) and Center for Research in Security Prices
CRSP databases. To maintain a balanced panel, we retain those stocks that
are present throughout the whole sample period. Stocks with an average share
3 Rosu (2009) develops a model that implicitly recognizes these technological advances and
simply assumes that limit orders can be constantly adjusted. Consistent with AT, Hasbrouck and
Saar (2009) find that by 2004 a large number of limit orders are cancelled within two seconds on
the INET trading platform.
4 Keim and Madhavan (1995) and Chan and Lakonishok (1995) study institutional orders that
are broken up.
5 Almgren and Chriss (2000) extend this optimization problem by considering the risk that
arises from breaking up orders and slowly executing them. Obizhaeva and Wang (2005) optimize
assuming that liquidity does not replenish immediately after it is taken but only gradually over
time. For each component of a larger transaction, a trader or algorithm must choose the type and
aggressiveness of the order. Cohen et al. (1981) and Harris (1998) focus on the simplest static
choice: market order versus limit order. However, a limit price must be chosen, and the problem is
dynamic; Goettler et al. (2009) model both aspects.


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price of less than $5 are removed from the sample, as are stocks with an average share price of more than $1,000. The resulting sample consists of monthly
observations for 943 common stocks. The balanced panel eliminates compositional changes in the sample over time, which could induce some survivorship
effects if disappearing stocks are less liquid. This could overstate time-series
improvements in liquidity, although the same liquidity patterns are present
without a survivorship requirement (see Comerton-Forde et al. (2010)).
Stocks are sorted into quintiles based on market capitalization. Quintile 1
refers to large-cap stocks and quintile 5 corresponds to small-cap stocks. All
variables used in the analysis are 99.9 % winsorized: values smaller than
the 0.05% quantile are set equal to that quantile, and values larger than the
99.95% quantile are set equal to that quantile.
A. Proxies for AT
We cannot directly observe whether a particular order is generated by a
computer algorithm. For cost and speed reasons, most algorithms do not rely on
human intermediaries but instead generate orders that are sent electronically
to a trading venue. Thus, we use the rate of electronic message traffic as a proxy
for the amount of AT taking place.6 This proxy is commonly used by market
participants, including consultants Aite Group and Tabb Group, as well as
exchanges and other market venues.7
For example, in discussing market venue capacity limits following an episode
of heavy trading volume in February 2007, a Securities Industry News report
quotes NASDAQ senior vice president of transaction services, Brian Hyndman,
who noted that exchanges have dealt with massive increases in message traffic
over the past 5 to 6 years, coinciding with algorithmic growth:
“It used to be one-to-one,” Hyndman said. “Then you’d see a customer
send ten orders that would result in only one execution. That’s because
the black box would cancel a lot of the orders. We’ve seen that rise from
20- to 30- to 50-to-one. The amount of orders in the marketplace increased

exponentially.”8
In the case of the NYSE, electronic message traffic includes order submissions,
cancellations, and trade reports that are handled by the NYSE’s SuperDOT
system and captured in the NYSE’s System Order Data (SOD) database. The
electronic message traffic measure for the NYSE excludes all specialist quoting,
as well as all orders that are sent manually to the floor and are handled by a
floor broker.
6

See Biais and Weill (2009) for theoretical evidence on how AT relates to message traffic.
See, e.g., Jonathan Keehner, “Massive surge in quotes, electronic messages may paralyse US
market,” June 14,
2007.
8 See Shane Kite, “Reacting to market break, NYSE and NASDAQ act on capacity,” Securities
Industry News, March 12, 2007.
7


Does Algorithmic Trading Improve Liquidity?

7

As suggested by the quote above, an important issue is whether and how
to normalize the message traffic numbers. The top half of Figure 1 shows the
evolution of message traffic over time. We focus on the largest-cap quintile
of stocks, as they constitute the vast bulk of stock market capitalization and
trading activity. Immediately after decimalization at the start of 2001, the
average large-cap stock sees about 35 messages per minute during the trading
day. There are a few bumps along the way, but by the end of 2005 there are an
average of about 250 messages per minute (more than 4 messages per second)

for these same large-cap stocks. We could, of course, simply use the raw message
traffic numbers, but there has been an increase in trading volume over the same
interval, and without normalization a raw message traffic measure may just
capture the increase in trading rather than the change in the nature of trading.
Therefore, for each stock each month we calculate our AT proxy, algo tradit , as
the number of electronic messages per $100 of trading volume.9 The normalized
measure still rises rapidly over the 5-year sample period, while measures of
market liquidity such as proportional spreads have declined sharply but appear
to asymptote near the end of the sample period (see, e.g., the average quoted
spreads in the top half of Figure 2 below), which occurs as more and more
stocks are quoted with the minimum spread of $0.01.
The time-series evolution of algo tradit is displayed in the bottom half of
Figure 1. For the largest-cap quintile, there is about $7,000 of trading volume
per electronic message at the beginning of the sample in 2001, decreasing
dramatically to about $1,100 of trading volume per electronic message by the
end of 2005. Over time, smaller-cap stocks display similar time-series patterns.
It is worth noting that our AT proxies may also capture changes in trading
strategies. For example, messages and algo tradit will increase if the same market participants use algorithms but modify their trading or execution strategies
so that those algorithms submit and cancel orders more often. Similarly, the
measure will increase if existing algorithms are modified to “slice and dice”
large orders into smaller pieces. This is useful, as we want to capture increases
in the intensity of order submissions and cancellations by existing algorithmic
traders, as well as the increase in the fraction of market participants employing
algorithms in trading.
B. Liquidity Measures
We measure liquidity using quoted half-spreads, effective half-spreads, 5minute and 30-minute realized spreads, and 5-minute and 30-minute price
impacts, all of which are measured as share-weighted averages and expressed
in basis points as a proportion of the prevailing midpoint. The effective spread
is the difference between the midpoint of the bid and ask quotes and the actual
9 Our results are virtually the same when we normalize by the number of trades or use raw

message traffic numbers (see Table IA.4 in the Internet Appendix, available online in the “Supplements and Datasets” section at The results are also the
same when we use the number of cancellations rather than the number of messages to construct
the AT measure.


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Figure 1. Algorithmic trading measures. For each market-cap quintile, where Q1 is the
largest-cap quintile, these graphs depict (i) the number of (electronic) messages per minute and (ii)
our proxy for algorithmic trading, which is defined as the negative of trading volume (in hundreds
of dollars) divided by the number of messages.


Does Algorithmic Trading Improve Liquidity?

9

Figure 2. Liquidity measures. These graphs depict (i) quoted half-spread, (ii) quoted depth,
and (iii) effective spread. All spread measures are share volume-weighted averages within-firm
that are then averaged across firms within each market-cap quintile, where Q1 is the largest-cap
quintile.


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Figure 2. Continued

transaction price. The wider the effective spread, the less liquid is the stock. For
the NYSE, effective spreads are more meaningful than quoted spreads because
specialists and floor brokers are sometimes willing to trade at prices within the
quoted bid and ask prices. For the tth trade in stock j, the proportional effective
half-spread, espreadjt , is defined as
espreadjt = q jt ( p jt − mjt )/mjt ,

(1)

where q jt is an indicator variable that equals +1 for buyer-initiated trades
and −1 for seller-initiated trades, p jt is the trade price, and mjt is the quote
midpoint prevailing at the time of the trade. We follow the standard tradesigning approach of Lee and Ready (1991) and use contemporaneous quotes
to sign trades and calculate effective spreads (see Bessembinder (2003), for
example). For each stock each day, we use all NYSE trades and quotes to calculate quoted and effective spreads for each reported transaction and calculate a
share-weighted average across all trades that day. For each month we calculate
the simple average across days. We also measure share-weighted quoted depth
at the time of each transaction in thousands of dollars.
Figure 2 shows quite clearly that our measures of liquidity are generally
improving over the sample period. Figure 1 shows that AT increases almost
monotonically. The spread measures are not nearly as monotonic, with illiquidity spikes in both 2001 and 2002 that correspond to sharp stock market
declines and increased volatility over the same sample period (see Figure IA.5
in the Internet Appendix). Nevertheless, one is tempted to conclude that these


Does Algorithmic Trading Improve Liquidity?

11

two trends are related. The analysis below investigates exactly this relationship using formal econometric tools.

If spreads narrow when AT increases, it is natural to decompose the spread
along the lines of Glosten (1987) to determine whether the narrower spread
means less revenue for liquidity providers, smaller gross losses due to informed
liquidity demanders, or both. We estimate revenue to liquidity providers using
the 5-minute realized spread, which assumes the liquidity provider is able
to close her position at the quote midpoint 5 minutes after the trade. The
proportional realized spread for the tth transaction in stock j is defined as
rspreadjt = q jt ( p jt − mj,t+5min )/mjt ,

(2)

where p jt is the trade price, q jt is the buy–sell indicator (+1 for buys, −1 for
sells), mjt is the midpoint prevailing at the time of the tth trade, and mj,t+5min is
the quote midpoint 5 minutes after the tth trade. The 30-minute realized spread
is calculated analogously using the quote midpoint 30 minutes after the trade.
We measure gross losses to liquidity demanders due to adverse selection
using the 5-minute price impact of a trade, adv selection jt , defined using the
same variables as
adv selection jt = q jt (mj,t+5min − mjt )/mjt .

(3)

The 30-minute price impact is calculated analogously. Note that there is an
arithmetic identity relating the realized spread, the adverse selection (price
impact), and the effective spread espreadjt
espreadjt = rspreadjt + adv selection jt .

(4)

Figure 3 graphs the decomposition of the two spread components. Both realized spreads, rspreadit , and price impacts, adv selectionit , decline from 2001

to 2005. Most of the narrowed spread is due to a decline in adverse selection
losses to liquidity demanders. Depending on the size quintile considered, 75%
to 90% of the narrowed spread is due to a smaller price impact.
So far, the graphical evidence shows time-series associations between AT
and liquidity. The natural way to formally test this association is by regressing
the various liquidity measures (Lit ) on AT (Ait ) and variables controlling for
market conditions (Xit ):
Lit = αi + β Ait + δ Xit + εit .

(5)

The problem is that AT is an endogenous choice made by traders. A trader’s
decision to adopt AT could depend on many factors, including liquidity. For
example, the evidence in Goldstein and Kavajecz (2004) indicates that humans
are used more often when markets are illiquid and volatile. Econometrically,
this means that the slope coefficient β from estimating equation (5) via OLS
is not an unbiased estimate of the causal effect of a change in AT on liquidity.
Unless we have a structural model, the only way to identify the causal effect is
to find an instrumental variable (IV) that affects AT but is uncorrelated with


12

The Journal of Finance R

Figure 3. Spread decomposition into liquidity supplier revenues and adverse selection.
These graphs depict the two components of the effective spread: (i) realized spread and (ii) the
adverse selection component, also known as the (permanent) price impact. Both are based on the
quote midpoint 5 minutes after the trade. Results are graphed by market-cap quintile, where Q1
is the largest-cap quintile.

Does Algorithmic Trading Improve Liquidity?

13

εit . Standard econometrics texts, for example, Greene (2007, Ch. 12), show that
under these conditions, the resulting IV estimator consistently estimates the
causal effect, in this case the effect of an exogenous change in AT on liquidity.
We discuss such an instrument in the next section.
III. Autoquote
In this section we provide an overview of our instrument, which is a change
in NYSE market structure that causes an exogenous increase in AT.
As a result of the reduction of the minimum tick to a penny in early 2001
as part of decimalization, the depth at the inside quote shrank dramatically.
In response, the NYSE proposed that a “liquidity quote” for each stock be
displayed along with the best bid and offer. The NYSE liquidity quote was
designed to provide a firm bid and offer for substantial size, typically at least
15,000 shares, accessible immediately.10
At the time of the liquidity quote proposal, specialists were responsible for
manually disseminating the inside quote.11 Clerks at the specialist posts on
the floor of the exchange were typing rapidly and continuously from open to
close and still were barely keeping up with order matching, trade reporting,
and quote updating. In order to ease this capacity constraint and free up specialists and clerks to manage a liquidity quote, the exchange proposed that it
“autoquote” the inside quote, disseminating a new quote automatically whenever there was a relevant change to the limit order book. This would happen
when a better-priced order arrived, when an order at the inside was canceled,
when the inside quote was traded with in whole or in part, or when the size of
the inside quote changed.
Note that the specialist’s structural advantages were otherwise unaffected
by autoquote. A specialist could still disseminate a manual quote at any time

in order to reflect his own trading interest or that of floor traders. Specialists
continued to execute most trades manually, and they could still participate
in those trades subject to the unchanged NYSE rules. NYSE market share
remains unchanged at about 80% around the adoption of autoquote.
Autoquote was an important innovation for algorithmic traders because an
automated quote update could provide more immediate feedback about the
potential terms of trade. This speedup of a few seconds would provide critical
new information to algorithms, but would be unlikely to directly affect the
trading behavior of slower-reacting humans. Autoquote allowed algorithmic
liquidity suppliers to, say, quickly notice an abnormally wide inside quote and
provide liquidity accordingly via a limit order. Algorithmic liquidity demanders
could quickly access this quote via a conventional market or marketable limit
order or by using the NYSE’s automated execution facility for limit orders of
10 For more details, the NYSE proposal is contained in Securities Exchange Act Release No.
47091 (December 23, 2002), 68 FR 133.
11 One exception: NYSE software would automatically disseminate an updated quote after 30
seconds if the specialist had not already done so.


14

The Journal of Finance R

Figure 4. Autoquote introduction. This graph depicts the staggered introduction of autoquote
on the NYSE. It graphs the number of stocks in each market-cap quintile that are autoquoted at
a given time. Quintile 1 contains largest-cap stocks.

1,099 shares or less. In the next section, we show that autoquote is positively
correlated with our AT measure, which is one of the requirements for autoquote
to be a valid instrument.

The NYSE began to phase in the autoquote software on January 29, 2003,
starting with six active, large-cap stocks. During the next 2 months, over 200
additional stocks were phased in at various dates, and all remaining NYSE
stocks were phased in on May 27, 2003.12 Figure 4 provides additional details
on the phase-in process. The rollout order was determined in late 2002. Early
stocks tended to be active large-cap stocks because the NYSE felt that these
stocks would benefit most from the liquidity quote. Beyond that criterion, conversations with those involved at the NYSE indicate that early phase-in stocks
were chosen mainly because the specialist assigned to that stock was receptive
to new technology.
The phase-in is particularly important to our empirical design. It allows
us to take out all market-wide changes in liquidity, and identify the causal
effect of AT by comparing autoquoted stocks to non-autoquoted stocks using
a difference-in-differences methodology. The IV methodology discussed below
incorporates data before and after each NYSE stock’s autoquote adoption so
the estimated effect of AT on liquidity incorporates every stock’s autoquote
12 Liquidity quotes were delayed due to a property rights dispute with data vendors, so they
did not become operational until June 2003, after autoquote was fully phased in. Liquidity quotes
were almost never used and were formally abandoned in July 2005.


Does Algorithmic Trading Improve Liquidity?

15

transition, whenever it occurs. Thus, even if the phase-in order is determined by
other unknown criteria, our empirical methodology remains valid in most cases.
For example, there is no bias if the phase-in is determined by the specialist’s
receptiveness to new technology, and this is correlated with the amount of AT
in his stocks. There are a small number of problematic phase-in scenarios,
however. We discuss these next.

For the staggered introduction of autoquote to serve as a valid instrument, it
must satisfy the exclusion restriction. Specifically, a stock’s move to autoquote
must not be correlated with the error term in that firm’s liquidity equation
(equation (5)). This does not mean that the autoquote rollout must be assigned
randomly. The liquidity equation includes a firm fixed effect, calendar dummies,
and a set of control variables. The instrument remains valid even if the rollout
schedule is related to these particular explanatory variables. For instance, if the
stocks chosen for early phase-in tend to have high mean liquidity, this would be
picked up by the firm fixed effect and the exclusion restriction would still hold.
In fact, due to the explanatory variables, the exclusion restriction is violated
only if the autoquote phase-in schedule is somehow related to contemporaneous
changes in firm-specific, idiosyncratic liquidity that are not due to changes in
AT.
Thus, it is quite helpful that the rollout schedule for autoquote was fixed
months in advance, as it seems highly unlikely that the phase-in schedule could
be correlated with idiosyncratic liquidity months into the future. The only way
this might happen is if there are sufficiently persistent but temporary shocks to
idiosyncratic liquidity. For example, if temporarily illiquid stocks are chosen for
early phase-in, these stocks might still be illiquid when autoquote begins, and
their liquidity would improve post-autoquote as they revert to mean liquidity,
thereby overstating the causal effect.13 To investigate this possibility, we study
the dynamics of liquidity using an AR(1) model of effective spreads for each
firm in the sample. Table IA.3 of the Internet Appendix shows that the average
AR(1) coefficient is 0.18, corresponding to a half-life of less than a day.14 We
also do not find statistical support for the conjecture that stocks that migrate
experience unusual liquidity just ahead of the migration. More precisely, the
predicted effective spread based on all information up until the day before the
introduction, including liquidity covariates, is not significantly different from
its unconditional mean. All of this supports the exogeneity of our instrument.
Lastly, the exclusion restriction requires autoquote to affect liquidity only

via AT. We have argued that autoquote’s time scale is only relevant for algorithms and that autoquote does not directly affect liquidity via nonalgorithmic
trading.15 However, we cannot test this conjecture using the available data.
13 Late phase-in stocks will not offset this effect. Even if late phase-in stocks are temporarily
liquid when chosen, this temporary effect has more time to die out by the time autoquote is
implemented for them.
14 Also because the average daily AR(1) coefficient is quite small, there is little scope for the bias
that can arise in dynamic panel data models with strong persistence. See, for example, Arellano
(2003, Ch. 6.2).
15 For example, autoquote could simply make the observed quotes less stale. We investigate


16

The Journal of Finance R

Thus, it is important to emphasize that our conclusions on causality rely on
the intuitively appealing but ultimately untestable assumption that autoquote
affects liquidity only via its effect on AT.
IV. AT’s Impact on Liquidity
To study the effects of autoquote, we build a daily panel of NYSE common
stocks. The sample begins on December 2, 2002, which is approximately 2
months before the autoquote phase-in begins, and it extends through July 31,
2003, about 2 months after the last batch of NYSE stocks moves to the autoquote regime. We use standard price filters: stocks with an average share price
of less than $5 or more than $1,000 are removed. To make our various autoquote analyses comparable, we use the same sample of stocks throughout this
section. The Hasbrouck (1991a,b) decomposition (discussed below in Section
VI) has the most severe data requirements, so we retain all stocks that have at
least 21 trades per day for each day in the 8-month sample period. This leaves
1,082 stocks in the sample. The shorter time period for the autoquote sample
allows for a larger balanced panel compared to the 5-year balanced panel used
to create Figures 1–3.

Next, we sort stocks into quintiles based on market capitalization. Quintile 1
refers to large-cap stocks and quintile 5 corresponds to small-cap stocks. Table I
contains means by quintile and standard deviations for all of the variables used
in the analysis. All variables used in the analysis are 99.9% winsorized: values
smaller than the 0.05% quantile are set equal to that quantile, and values
larger than the 99.95% quantile are set equal to that quantile.
Autoquote clearly leads to greater use of algorithms. Figure 1 shows that
message traffic increases by about 50% in the most active quintile of stocks as
autoquote is phased in. It is certainly hard to imagine that autoquote would
change the behavior of humans by anything close to this magnitude. However,
nowhere in the paper do we rely on this time-series increase in AT. Instead,
we include stock fixed effects and time fixed effects (day dummies), so that we
identify the effect of the market structure change via its staggered introduction.
The presence of these two-way fixed effects means that we are comparing the
changes experienced by autoquoted stocks to the changes in not-yet-autoquoted
control stocks.
We begin by estimating the following first-stage regression:
Mit = αi + γt + β Qit + εit ,

(6)

where Mit is the relevant dependent variable, for example, the number of electronic messages per minute, Qit , is the autoquote dummy set to zero before the

this possibility in Section I of the Internet Appendix and find that this mechanical explanation is
unlikely to account for our results.


Table I

share volume-weighted quoted half-spread (bps)

share volume-weighted depth ($1,000)
share volume-weighted effective half-spread
(bps)
share volume-weighted realized half-spread,
5min (bps)
share volume-weighted adverse selection
component half-spread, 5min,
“effective-realized” (bps)
#electronic messages per minute, a proxy for
algorithmic activity (/minute)
dollar volume per electronic message times (−1),
a proxy for algorithmic trading ($100)
average daily volume ($million)
#trades per minute (/minute)
(annualized) share turnover
standard deviation open-to-close returns based
on daily price range, that is, high minus low,
Parkinson (1980), (%)
daily closing price ($)
shares outstanding times price ($billion)
trade size ($1,000)
specialist participation rate (%)

qspreadit
qdepthit
espreadit

# observations: 1,082 ∗ 167 (stock ∗ day)

priceit

market capit
trade sizeit
specialist participit

dollar volumeit
tradesit
share turnoverit
volatilityit

algo tradit

messagesit

adv selectionit

rspreadit

Description (Units)

Variable

CRSP
CRSP
TAQ
SOD

TAQ
TAQ
TAQ/CRSP
CRSP

40.01
28.99
37.56
13.07

94.71
5.72
1.11
1.47

32.05
4.09
19.41
12.97

24.09
2.92
1.52
1.56

−10.99

−18.44

TAQ/SOD

53.90

119.30

SOD

3.35

1.44

2.42

1.21

TAQ

6.82
41.85
4.79

Mean
Q2

TAQ

5.19
71.22
3.63

Mean
Q1

TAQ

TAQ
TAQ

Source

25.86
1.71
13.06
13.08

10.12
1.78
1.48
1.63

−8.05

29.81

4.69

1.88

9.17
31.43
6.56

Mean
Q3

23.93
0.90
9.73
13.73

5.32
1.24
1.45
1.74

−6.39

19.33

6.50

1.97

11.68
24.12
8.46

Mean
Q4

16.41
0.41
6.61
15.84

2.17
0.72
1.30
2.06

−4.61

10.44

10.16

4.34

19.89
15.76
14.50

Mean
Q5

3.46
1.96
8.03
3.92

22.72
0.72
1.16
0.85

4.54

15.55

5.12

4.71

4.84
23.42
3.73

St. Dev.
Within

This table presents summary statistics on daily data for the period December 2002 through July 2003. This period covers the phase-in of autoquote,
used as an instrument in the instrumental variable analysis. The data set combines TAQ, CRSP, and the NYSE System Order Data (SOD) database.
The balanced panel consists of 1,082 stocks sorted into quintiles based on market capitalization, where quintile 1 contains largest-cap stocks. All
variables are 99.9% winsorized. The within standard deviation is based on day t’s deviation relative to the time mean, that is, xit∗ = xit − xi .

Summary Statistics

Does Algorithmic Trading Improve Liquidity?
17


18

The Journal of Finance R
Table II

Autoquote Impact on Messages, Algorithmic Trading Proxy, and
Covariates
This table shows the impact of autoquote on other variables, and the second column can be interpreted as the first-stage instrumental variables regression when algo tradit is the dependent
variable. The analysis is based on daily observations from December 2002 through July 2003,
which covers the phase-in of autoquote. We regress each of the variables used in the IV analysis
on the autoquote dummy (auto quoteit ) using the following specification:
Mit = αi + γt + β Qit + εit ,
where Mit is the relevant dependent variable, for example, the number of electronic messages
per minute, Qit is the autoquote dummy set to zero before the autoquote introduction and one
afterward, αi is a stock fixed effect, and γt is a day dummy. There are also separate regressions
for each size quintile, and statistical significance is based on standard errors that are robust
to general cross-section and time-series heteroskedasticity and within-group autocorrelation (see
Arellano and Bond (1991)). Table I provides other variable definitions. ∗/∗∗ denote significance at
the 95%/99% level.

messagesit

algo
tradit

share
turnoverit

volatilityit

Slope coefficient from regression of column variable on auto
All
2.135∗∗
0.291∗∗

−0.016∗∗
Q1 (largest cap)
6.286∗∗
0.414∗∗
0.016∗
∗∗
∗∗
Q2
0.880
0.396
−0.029∗
Q3
0.944∗∗
0.292∗∗
0.002
Q4
0.223∗∗
0.029
−0.006
Q5 (smallest cap)
−0.031
0.219∗∗
−0.080∗∗

quoteit
0.001
−0.003
0.007
−0.001
−0.003

0.003

1/ priceit

ln market
capit

0.000∗∗
0.000∗∗
−0.000∗∗
0.000
−0.000
0.002∗∗

−0.003∗∗
−0.005∗∗
0.003∗∗
−0.004∗∗
0.002
−0.013∗∗

autoquote introduction and one afterward; αi is a stock fixed effect; and γt is a
day dummy. There are also separate regressions for each size quintile.
Table II reports the slope coefficients for this specification. When the dependent variable Mit is the number of electronic messages per minute for stock
i on day t, we find a significant positive relationship. The coefficient of 2.135
on autoquote implies that autoquote increases message traffic by an average
of two messages per minute. In December 2002, the month before autoquote
begins its rollout, our sample stocks average 36 messages per minute, so autoquote causes a 6% increase in message traffic on average. Associations are
stronger for large-cap stocks, consistent with the conventional wisdom that AT
was more prevalent at the time for active, liquid stocks.

Table II also shows that there is a significant positive relationship between
the autoquote dummy and our preferred measure of AT, algo tradit , which is
the negative of dollar volume in hundreds per electronic message. Thus, it is
clear that autoquote leads to more AT in all but the smallest quintiles.16 There
16 In the IV regressions in Tables III to V we report F statistics that reject the null that the
instruments do not enter the first-stage regression. Bound et al. (1995, p. 446) mention that “F
statistics close to 1 should be cause for concern.” Our F statistics range from 5.88 to 7.32, and we
are thus not afflicted with a weak instruments problem.


Does Algorithmic Trading Improve Liquidity?

19

is no consistent relationship between autoquote and any other variable, such
as turnover, volatility, and share price.
Our principal goal is to understand the effects of algorithmic liquidity supply
on market quality, and so we use the autoquote dummy as an instrument
for AT in a panel regression framework. Our main instrumental variables
specification is a daily panel of 1,082 NYSE stocks over the 8-month sample
period spanning the staggered implementation of autoquote. The dependent
variable is one of five liquidity measures: quoted half-spreads, effective halfspreads, realized spreads, or price impacts, all of which are share volumeweighted and measured in basis points, or the quoted depth in thousands of
dollars. We include fixed effects for each stock as well as time dummies, and we
include share turnover, volatility based on the daily price range (high minus
low, see Parkinson (1980)), the inverse of share price, and the log of market
cap as control variables. Results are virtually identical if we exclude these
control variables. Based on anecdotal information that AT was relatively more
important for active large-cap stocks during this time period, we estimate this
specification separately for each market-cap quintile.
The estimated equation is

Lit = αi + γt + β Ait + δ Xit + εit ,

(7)

where Lit is a spread measure for stock i on day t, Ait is the AT measure
algo tradit , and Xit is a vector of control variables, including share turnover,
volatility, the inverse of share price, and log market cap. We always include
fixed effects and time dummies. The set of instruments consists of all explanatory variables, except that we replace algo tradit with auto quoteit . Inference
is based on standard errors that are robust to general cross-section and timeseries heteroskedasticity and within-group autocorrelation (see Arellano and
Bond (1991)). Section II of the Internet Appendix shows that the IV regression
is unaffected by the use of a proxy for AT, as long as the noise in the proxy is
uncorrelated with the autoquote instrument.
The results are reported in Panel A of Table III and the most reliable effects
are in larger stocks. For large-cap stocks (quintiles 1 and 2), the autoquote
instrument shows that an increase in algorithmic liquidity supply narrows
both the quoted and effective spread. To interpret the estimated coefficient
on the AT variable, recall that the AT measure algo tradit is the negative of
dollar volume per electronic message, measured in hundreds of dollars, while
the spread is measured in basis points. Thus, the IV estimate of −0.53 on
the AT variable for quintile 1 means that a unit increase in AT, for example,
from the sample mean of $1,844 to $1,744 of volume per message, implies
that quoted spreads narrow by 0.53 basis points.17 The average within-stock
standard deviation for algo tradit is 4.54, or $454, so a one-standard deviation
17 Table IA.4 in the Internet Appendix contains additional analysis showing that the message
traffic component of algo tradit drives the decline in spreads.


Table III

espreadit

qdepthit

qspreadit

Q2

−0.42∗∗
(−2.21)
−1.43
(−1.16)
−0.32∗∗
(−2.23)

Q1

−0.53∗∗
(−3.23)
−3.49∗∗
(−2.51)
−0.18∗∗
(−2.67)

Q4

Q5

share turnoverit

volatilityit

−0.43
(−1.44)
−1.99
(−1.07)
−0.35
(−1.56)

−0.21
(−0.06)
15.60
(0.39)
−1.67
(−0.42)
9.92
(1.22)
0.61
(0.19)
4.65
(1.16)

−2.81∗∗
(−2.98)
−5.22
(−0.64)
−1.01∗∗
(−2.30)

0.90∗∗
(9.71)

−1.64∗
(−1.86)
0.69∗∗
(9.39)

108.40∗∗
(7.42)
−3.44
(−0.02)
72.77∗∗
(10.80)

1/ priceit

Coefficients on Control Variables

Panel A: Quoted Spread, Quoted Depth, and Effective Spread

Q3

Coefficient on algo tradit

(continued)

−3.61∗∗
(−2.28)
12.01
(0.82)
−1.30
(−1.46)

ln mkt capit

where Lit is a spread measure for stock i on day t, Ait is the algorithmic trading measure algo tradit , and Xit is a vector of control variables,
including share turnover, volatility, 1/price, and log market cap. Coefficients for the control variables and time dummies are quintile-specific. Market
cap–weighted coefficients are reported for the control variables. Fixed effects and time dummies are included. The set of instruments consists of
all explanatory variables, except that algo tradit is replaced with auto quoteit . There are separate regressions for each size quintile, and t-values in
parentheses are based on standard errors that are robust to general cross-section and time-series heteroskedasticity and within-group autocorrelation
(see Arellano and Bond (1991)). ∗/∗∗ denote significance at the 95%/99% level. In Panel B the suffix indicates the effective spread for a particular
trade size category: “1” if 100 shares ≤ trade size ≤ 499 shares; “2” if 500 shares ≤ trade size ≤ 1,999 shares; “3” if 2,000 shares ≤ trade size ≤ 4,999
shares; “4” if 5,000 shares ≤ trade size ≤ 9,999 shares; “5” if 9,999 shares < trade size.

Lit = αi + γt + β Ait + δ Xit + εit ,

This table regresses various measures of the (half) spread on our algorithmic trading proxy. It is based on daily observations from December 2002
through July 2003, which covers the phase-in of autoquote. The nonsynchronous autoquote introduction instruments for the endogenous algo tradit
to identify causality from algorithmic trading to liquidity. The specification is

Effect of Algorithmic Trading on Spread

20
The Journal of Finance R


0.35∗∗
(3.52)
−0.53∗∗
(−3.56)
0.33∗∗
(2.82)

−0.51∗∗
(−3.43)

(−2.02)
−0.30∗∗
(−2.61)
−0.26
(−1.42)
−0.29
(−0.90)
−0.32
(−1.04)

(−3.06)
−0.22∗∗
(−3.25)
−0.25∗∗
(−2.76)
−0.11
(−1.31)
−0.04
(−0.45)

Q5

share turnoverit

−0.17
(−1.09)
−0.41

(−1.62)
−0.66
(−1.57)
−0.24
(−0.62)
−0.36
(−0.86)

−1.83
(−0.44)
−4.21
(−0.45)
−3.27
(−0.36)
643.14
(0.00)
1.45
(0.26)
4.99
(1.20)
4.21
(1.16)
6.89
(1.13)
−3.27
(−0.38)
10.48
(0.25)

−0.83∗∗

(−2.70)
−1.62∗∗
(−2.68)
−1.85∗∗
(−2.67)
28.99
(0.00)
−0.21
(−0.25)

Panel B: Effective Spread by Trade Size Category

Q4

0.28∗∗
(3.91)
0.43∗∗
(2.80)
0.64∗∗
(4.03)
−10.35
(−0.00)
1.05∗∗
(4.01)

volatilityit

0.76∗∗
(3.97)
−1.07∗∗

(−4.08)
0.47∗
(1.94)
−0.81∗∗
(−2.76)

1.03∗∗
(2.06)
−1.39∗∗
(−2.06)
0.91
(1.61)
−1.27∗
(−1.80)
14.25
(0.46)
−15.51
(−0.47)
11.11
(0.47)
−12.60
(−0.47)

15.88
(1.36)
−11.21
(−1.33)
12.63
(1.31)
−8.28

(−1.25)

3.13∗
(1.92)
−4.12∗∗
(−2.23)
2.69∗∗
(1.98)
−3.66∗∗
(−2.33)

−1.06∗∗
(−2.15)
1.76∗∗
(3.28)
−2.33∗∗
(−5.99)
3.02∗∗
(6.91)

Panel C: Spread Decompositions Based on 5-Min and 30-Min Price Impact

−0.14∗∗

−0.12∗∗

Q3

45.81∗∗
(4.14)

26.65∗
(1.84)
52.24∗∗
(4.21)
20.21
(1.35)

50.45∗∗
(12.43)
53.84∗∗
(8.15)
61.48∗∗
(7.55)
224.78
(0.01)
73.01∗∗
(8.06)

1/ priceit

Coefficients on Control Variables

#observations: 1,082 ∗ 167 (stock ∗ day)
F test statistic of hypothesis that instruments do not enter first-stage regression: 7.32 (F(5, 179,587)), p-value: 0.0000

adv selection 30mit

rspread 30mit

adv selectionit

rspreadit

espread5it

espread4it

espread3it

espread2it

espread1it

Q2

Q1

Coefficient on algo tradit

Table III—Continued

5.05
(1.18)
−6.30
(−1.34)
2.83
(0.83)
−4.10
(−1.05)

−1.10
(−1.61)
−2.24
(−1.58)
−1.96
(−1.29)
93.04
(0.00)
0.12
(0.05)

ln mkt capit

Does Algorithmic Trading Improve Liquidity?
21


22

The Journal of Finance R

change in our AT measure is associated with a 4.54 ∗ 0.53 = 2.41 basis point
change in proportional spreads. This represents nearly a 50% decline from the
mean quoted spread of 5.19 basis points for quintile 1.
In the spirit of an event study, we also estimate an analogous non-IV panel
regression with the autoquote dummy directly on the right-hand side. We do not
report these results, but quoted and effective spreads are reliably narrower for
the three largest quintiles. For quintile 1, quoted spreads are 0.50 basis points
smaller (t = −9.18) after the autoquote introduction, and effective spreads are
0.17 basis points smaller (t = −4.33). Effective spreads narrow even more for

quintiles 2 and 3 by 0.21 and 0.23 basis points, respectively.
The IV estimate on algo tradit is statistically indistinguishable from zero for
quintiles 3 through 5. This could be a statistical power issue. Figure 4 above
shows that most small-cap stocks were phased in at the very end, reducing the
nonsynchronicity needed for econometric identification. Perhaps as a result,
the autoquote instrument is only weakly correlated with AT in these quintiles.
Alternatively, it could be the case that algorithms are less commonly used in
these smaller stocks, in which case the introduction of autoquote might have
little or no effect on these stocks’ market quality.
Quoted depth also declines with autoquote. One might worry that the narrower quoted spread simply reflects the smaller quoted quantity, casting doubt
on whether liquidity actually improves after autoquote is introduced. Here, a
calibration exercise is useful. The results for quintile 1 indicate that a one-unit
increase in the AT variable—a $100 decrease in trading volume per message—
reduces the quoted spread by 10%, as the average quoted spread from Table I
is 5.19 basis points. The same change reduces the quoted depth by about 5%,
based on an average quoted depth of $71,220. A small liquidity demander is
unaffected by the depth reduction and is unambiguously better off with the
narrower spread. A liquidity demander who trades the average quoted depth
of $71,220 is probably better off as well. She pays 10% less on 95% of her order,
and as long as she pays less than 290% of the original spread on the remaining
5% of her order, she is better off overall. Based on the $40.01 average share
price for this quintile, the average 5.19 basis point quoted spread translates
to 2.1 cents. For these stocks, it seems extremely unlikely that the last 5% of
her trade executes at a spread of more than 6.1 cents. Most likely, this last 5%
would execute only one cent wider. This makes it quite clear that the depth
reduction is small relative to the narrowing of the spread, at least for these
trade sizes.
To further explore the decline in depth, in Panel B of Table III we check to
see whether autoquote is associated with narrower effective spreads after controlling for trade size. Specifically, we sort trades into bins based on trade size,
calculate effective spreads for these trades alone, and estimate the IV specification for each trade size bin. For the larger quintiles, AT significantly narrows

the effective spread for all trade sizes below 5,000 shares. Point estimates go in
the same direction for the largest trade sizes but are not reliably different from
zero. In any case, we only have transaction-level data, not order-level data, so
it is not possible to conduct the analysis on large orders, given that large or-